A Novel AI Framework for WBAN Event Correlation in Healthcare: ServiceNow AIOps approach

Wireless Body Area Networks (WBANs) are one of the most innovative and advanced sensor networks that are revolutionizing healthcare monitoring; however, effective monitoring and correlating real-time physiological data streams are challenging. We present a new AI-driven framework using the ServiceNow AIOps platform to handle the aforementioned challenges. Using WBAN infrastructure applications, the proposed framework can handle different data streams in real time. Our framework identifies, categorizes, and prioritizes health incidents using rule-based filtering and advanced machine learning approaches. The ServiceNow platform’s event correlation engine can recognizes critical patterns and relationships among events, logs, and alerts and enables early detection, proactive intervention, and preemptive notification. With this integration, the platform receives multiple types of health data and analyzes, groups, classes, and responds to actionable events based on advanced analytics and predictive intelligence algorithms. A systematic approach was used to measure and analyze crucial success factors, such as the accuracy of event detection and the response time. The findings indicate that ServiceNow AIOPS can significantly enhance the capabilities of WBANs in terms of event correlation. Notable improvements were observed in the event detection accuracy and response times compared to traditional standalone monitoring systems. The platform’s adaptability to different WBAN sensor configurations and its scalable architecture are key advantages. In addition, the integration of machine learning algorithms augments the predictive intelligence capabilities, thereby reducing health event response times and enhancing overall reliability. The proposed research substantially contributes to the field by addressing the existing challenges in real-time WBAN monitoring, particularly in dynamic and data-intensive environments.